ADVERTISEMENT

When Money Talks, Watson Listens

Financial institutions are already familiar with integrating narrow artificial intelligence into their work. The algorithmic intelligences that currently work within the stock markets – designed to exploit tiny differences in value at lightning speed – now account for more than 75 percent of trades.

However, computer scientists at IBM want to help make such decision making machines – and others – smarter. They’ve developed a cognitive intelligence system known as IBM Watson™. Watson is a very different form of intelligence. Where algorithmic AI systems leverage the speed of computer systems to make decisions faster than a human could – without understanding the larger implications of those decisions – cognitive systems absorb human learning to support human processes.

READ NEXT

Join Vivienne Westwood and Adwoa Aboah being BOLD in Berlin

ByWIRED Insider

“The challenge for finance is that information that could be relevant to any financial decision could be scattered across a huge number of different places.,” says David Robson, Watson Director of Financial Services in Europe. “Finance is not a closed system – it’s affected by world events, by national legislation, even by the emotions of the people involved. That needs far more than rapid calculation – it needs a system that can learn and understand.”

ADVERTISEMENT

READ NEXT

Shakespeare’s sprite takes flight as an Intel-crafted digital avatar

In partnership withIntel

To be able to talk usefully with customers and finance workers, Watson first has to understand – which it does by reading millions of pages of text. Interacting with humans – through a dedicated training process and then in the real world – provides Watson with a growing understanding of what this mass of information means, and how it relates. And, across a range of possible applications, Watson learns how to deliver better answers.

The cognitive experience

When many banking customers are looking for preliminary information they ask their bank a question. Responding in a chat window or by email (or on an automated phone system using speech-to-text software), Watson applies a series of processes. First, Watson Natural Language Classifier Service applies its understanding of banking to the question. A simple automated system can recognise a word like “mortgage”, but Watson goes deeper into the context – is the customer looking for a mortgage? Asking for information on their mortgage? Or do they want to make a complaint?

Watson’s Dialog Service then draws out more information by asking and answering questions. Agents can leverage Watson’s acquired knowledge and ability to communicate in natural language. So, if the language and context have made it clear that the customer is looking for a mortgage, the dialogue process both extracts more information and gives the agent more help in answering customers’ questions.

ADVERTISEMENT

READ NEXT

Meditation and mindfulness: what matters to Thread founder Kieran O’Neill

In partnership withBraun

Mortgage decisions are complex, and are for many people their largest single financial product, so careful inquiry is vital. Do they want a repayment or an offset mortgage? How large is their deposit? What is the total value of the loan? All of these are potentially significant questions, pointing not just to a yes/no answer but affecting the desirability of different approaches and recommendations.

When an unexpected situation arises, Watson Retrieve and Rank function applies machine learning to its search for the right response. The goal is to be able to provide better, more complete answers, without customers having to search for the correct information.

When people are trying to do preminary research, Watson Tone Analyser is able to examine written communication in real-time to identify specific emotions, behaviour types and styles of communication – so, if a customer is frustrated or upset, they can get a conciliatory response, and if necessary an escalation, rather than a one-size-fits all response.

Growing knowledge

Watson is distinguished by its ability to understand, reason and learn – and so, while answering finance-specific questions draws on a huge dataset, more data can be added to create a more flexible, powerful and useful set of services. Watson Explorer is able to analyse structured content – the ordered data provided by bank accounts and balance sheets – and unstructured data such as news stories, email and chat transcripts, and represent them on a single screen.

READ NEXT

Meditation and mindfulness: what matters to Thread founder Kieran O’Neill

In partnership withBraun

Traditionally, personal banking service – “wealth management” – has been available only to the wealthiest customers. Meanwhile, retail banks are under pressure from challenger banks and fintech startups that can court customers with new services without the cost or the inertia of legacy technologies. One of their growth opportunities is to offer more personal, more effective money management and investment advice.

Watson Explorer can help bank staff and wealth managers provide a picture of their client’s personality type, comfort with risk, financial status and financial objectives, delivered on a single, easily referenced screen.

“Watson won’t buy you a nice lunch,” notes David Robson wryly, “but it can provide the kind of service only a small number of banking customers have access to now to a far larger number of people. We’re seeing a transformation in financial services, with a massive increase in the amount of structured data relevant to each customer, and with unstructured data becoming readable, understandable and applicable through Watson.

“Successful transformation requires visionaries – the players who make their transformations work will share an entrepreneurial nature, and a readiness to experiment.”

READ NEXT

Running, yoga, design: what matters to District Vision's Max Vallot and Tom Daly

In partnership withBraun

Explorer in action: Westpac One

Using Watson Explorer, the New Zealand-based subsidiary of Westpac offers customers all their data – account information, credit card debt, mortgages and their entire banking life – on a single screen of their mobile app, Westpac One. Every time a customer logs in, they are given instant, real-time data on the health of their finances, drawn from millions of customer transactions.

“The implementation wasn’t a traditional ‘buy and install’,” explains Jason Millett, who served as CIO when Westpac One was created. “Instead, it was set up as a service, with the Watson engine delivering the critical functionality – ingesting 84 months of data, and then presenting the data at sub-second speeds.”

The speed allows Westpac to deliver complex financial information through an easily understood user interface, where the customer can easily filter and search through their timeline for insight into their financial health and planning.

This is a classic example of moving away from systems of record to systems of insight and engagement, that put more functionality closer to the customer,” Millett adds. “This opens the market to greater innovation and disruption.”

READ NEXT

Running, yoga, design: what matters to District Vision's Max Vallot and Tom Daly

In partnership withBraun

Connecting the dots

The cognitive technology powering Watson has many applications, and along with finance one of the most exciting areas has been health, where Watson’s ability to read the vast amount of medical literature produced every day has created a promising source of treatment planning support for doctors.

Because Watson lives in the cloud, information can be combined across business sectors - creating new opportunities and in some cases new business models.

The reinsurance sector is one area where, currently, actuaries and other experts aim to make an informed guess about a client’s potential risk, based on a limited amount of individual information and huge volumes of statistical analysis, amended and revisited over decades. Underwriting is an inexact science – people are more complicated than even comprehensive cohort analysis.

Improving the accuracy of reinsurance risk calculation – and therefore being able to set competitive premiums – is one of the insurance industry’s holy grails, and there are already services designed to improve premiums for drivers prepared to upload their driving behaviour using the Internet of Things. In contrast to these limited, user-focused activities, Watson can digest massive amounts of information, and reveal diverse insights by cross-referencing huge data sets.

READ NEXT

From AI caddies to grip guides and swing sensors: how data is driving golf forward

In partnership withMicrosoft Cloud

Cognitive focus: Swiss Re

“I want to buy my daughter a hundred years of good life. Not a perfect life – I can’t afford that – but a good life.”

This isn’t the way people normally talk about insurance. But Rainer Baumann, CIO for Information at the insurance and reinsurance giant Swiss Re, believes that the way we insure and are insured is undergoing a transformation – a transformation powered by the need for understanding.

“At the moment, primary insurers hold a lot of information on their customers – questionnaires, handwritten notes, and so on,” Baumann explains. “Reinsurers only receive a fraction of that data – but have just as great a need to understand how the world works.”

The gaps in that data-drawn picture affect both the information reinsurers have at hand, and also how that information can be leveraged. In complex claim cases, which include many factors, digging through even incomplete records can take months - often more time than it is worth for a relatively small claim. A cognitive system would be able to absorb those records almost instantaneously, and make them available to a time pressured human agent.

The benefit from cognitive systems would extend past efficiency, however, according to Baumann, to changing the whole insurance model. At the moment, for example, personal insurance is usually sold, not bought, and is produced in standard rather than tailored packages. A digital advisor using a cognitive system can fill those gaps, and advise customers and agents on their risk profiles.

From there, the next step is Insurance as a Service. Baumann uses the example of Uber. First Uber and services like it disrupted the models of car ownership and traditional taxi services. Soon, autonomous vehicles will disrupt that disruption in turn. “Transport” as a service may not involve owning a car, but paying for the confidence that you can get from A to B when you need to – with insurance, maintenance and other costs factored into a subscription. As technology changes, the way that ability to travel is delivered will change, but the proposition – being able to move from place to place – will not.

No current insurance product can follow a person born today through potentially 100 years of life, changing and adapting its terms across different locations, lifestyles, medical conditions and many other factors. But that is the potential future.

Swiss Re has begun by founding a Centre of Competence, to examine the applications of Watson’s cognitive approach, and is developing products powered by Watson. The often cautious insurance sector will need to respond to a changing world; Watson is already helping Swiss Re to adapt.

Taking the work from paperwork

Insurance, banking and the whole financial sector are famously heavily regulated – to the point where often institutions struggle to keep up with new rules, despite the risk of fines.

Huge amounts of computing power have been brought to bear on solving this problem, but, without understanding, these solutions struggle to be more than more efficient ways to document and search. Watson’s capacity to learn and understand, and to relate new regulations both to past activities and to future plans, changes the model of computer-based support from a search-driven repository of knowledge to an active and highly informed partner.

ADVERTISEMENT

Overstretched compliance departments could consult with Watson for immediate recommendations on a planned activity, or for support in unfamiliar national compliance regimes. In March of this year KPMG announced that it would be using Watson to analyse financial data to improve its auditing process.

Future finance

As Watson becomes more familiar with more areas of finance, new markets such as pensions and tax will open up – ensuring that customers get the best available deals, and managers the best information and understanding of their customers

Watson’s promise for the financial sector is simple: better service in the front office, and better decision-making at deeper levels. However, with an industry as large and as far-reaching as finance, even minor changes can have huge effects. With the power of cognitive computing, financial institutions have the opportunity to lead the transformation.